[3S5-GS-2] Machine learning:

[3S5-GS-2-05] Simulating task performances of Behavioral Inattention Test (BIT) of patients with hemineglect

〇Shinichi Asakawa¹, Muto Jun² (1. Tokyo Woman's Christian University, 2. FUJITA Health University )

Keywords:hemi-neglect, attention, transfer learning

We propose a neural network model to mimic the task performances of patients with hemi-spatial neglect. The model proposed here is capable of performing several tasks of the BIT (Behavioral Ignorance Test) adopted in clinical practices. The model applied a domain adaptation for the testing tasks by transfer learning of the pre-trained model for ResNet. It has also references to the attention models such as computational neuroscience and cognitive science. These conventional models have assumed the allocation of attention across both left and right visual fields: a dorsal ‘where’ pathway for processing object locations in the visual field, and a ventral ‘what’ pathway to d eal with object recognitions, and also circuits between the parietal and frontal lobes to integrate this kind of information. The proposed model can mimic the patient's task performance due to the lesion in the right parietal lobe damag es. Compared to the conventional model, our model cab be easily compared to the patient's performance and the mode l's parameters easily interpreted. This makes it possible to consider the relationship with the attention model in the natural language processin g model.